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Title: Improved face mask detection with super-resolution techniques
Authors: Suresh, Prem Adithya
Keywords: Engineering::Computer science and engineering
Issue Date: 2021
Publisher: Nanyang Technological University
Source: Suresh, P. A. (2021). Improved face mask detection with super-resolution techniques. Final Year Project (FYP), Nanyang Technological University, Singapore.
Project: SCSE20-0347
Abstract: Super-Resolution is the process of reconstructing a low resolution image into a high resolution image. In recent years, many deep learning based techniques have surfaced and as a result, super-resolution has become a competitive field spurring the proposal of many state-of-the-art models. Super-Resolution can potentially have many applications and one such application, which is especially relevant during this COVID-19 pandemic, is face mask detection. Face mask detection has been implemented rapidly around the world since the start of the pandemic and this project shows that super-resolution techniques help improve the accuracy of face mask detection. Three models which are SSD based models enhanced with the addition super-resolution layers are pitted against the baseline model without super-resolution layers present. All models were trained, validated and tested on a dataset containing 14,016 images of masked and unmasked faces. All of the proposed models beat the baseline model’s mean average precision (mAP) of 76.73% where the best mAP achieved was 80.69%.
Schools: School of Computer Science and Engineering 
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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